SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation

• URL: http://arxiv.org/abs/2310.09424v1
• Date: Fri, 13 Oct 2023 22:07:33 GMT
• Title: SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation
• Authors: Zhehuai Chen, He Huang, Andrei Andrusenko, Oleksii Hrinchuk, Krishna C. Puvvada, Jason Li, Subhankar Ghosh, Jagadeesh Balam, Boris Ginsburg
• Abstract summary: We present a novel Speech Augmented Language Model (SALM) with em multitask and em in-context learning capabilities. The unified SALM achieves performance on par with task-specific Conformer baselines for Automatic Speech Recognition (ASR) and Speech Translation (AST)
• Score: 26.778332992311043
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a novel Speech Augmented Language Model (SALM) with {\em multitask} and {\em in-context} learning capabilities. SALM comprises a frozen text LLM, a audio encoder, a modality adapter module, and LoRA layers to accommodate speech input and associated task instructions. The unified SALM not only achieves performance on par with task-specific Conformer baselines for Automatic Speech Recognition (ASR) and Speech Translation (AST), but also exhibits zero-shot in-context learning capabilities, demonstrated through keyword-boosting task for ASR and AST. Moreover, {\em speech supervised in-context training} is proposed to bridge the gap between LLM training and downstream speech tasks, which further boosts the in-context learning ability of speech-to-text models. Proposed model is open-sourced via NeMo toolkit.

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